Hyeonjae Cheon

A simulation-based optimal design and analysis method for designing a train overhaul maintenance facility

This paper presents a simulation-based optimal design and analysis method for designing a train overhaul maintenance facility. Because the train is composed of one or more cars, the authors design the simulation model after analysing the operation of the train according to the trains following parts: train, car, car body, and wheel. The authors consider the critical (dependent) factors and design (independent) parameters for the simulation analysis. Therefore, multi-criteria decision making (MCDM) is proposed for selecting alternatives, and simulation optimization is used for finding the optimal design parameters of the selected alternative. A case study for the above approach is applied to the electric locomotive overhaul maintenance facility. This paper provides a comprehensive framework for a train overhaul maintenance facility design using MCDM and the simulation optimization.

Opinion leader based filtering

Recommendation systems are helping users find the information, products, and other people they most want to find, therefore many on-line stores provide recommending services e.g. Amazon, CDNOW, etc. Most recommendation systems use collaborative filtering, content-based filtering, and hybrid techniques to predict user preferences. We discuss the strengths and weaknesses of the techniques and present a unique recommendation system that automatically selects opinion leaders by category or genre to improve the performance of recommendation. Finally, our approach will help to solve the cold-start problem in collaborative filtering.

Development of Secure Entrance System using AOP and Design Pattern

Abstract A secure entrance system is complicated because it should have various functions like monitoring, logging, tracing, authentication, authorization, staff locating, managing staff enter-and-leave, and gate control. In this paper, we built and applied a secure entrance system for a domestic nuclear plant using Aspect Oriented Programming(AOP) and design pattern. Using AOP has an advantage of clearly distinguishing the role for each functional module because building a system separated independently from the systems business logic and security logic is possible. It can manage system alternation flexibility by frequent change of external environment, building a more flexible system based on increased code reuse, efficient functioning is possible which is an original advantage of AOP. Using design pattern enables to design by structuring the complicated problems that arise in general software development. Therefore, the safety of the system can also be guaranteed. Key Words :

Implementation of a service oriented architecture based on JXTA for new business models (ICCAS 2007)

This paper presents the new type of SOA Framework that can provide smooth service in the B2C and C2C environment unlike its predecessor which mainly considers the B2B environment. To this end, this SOA has been established with the use of JXTA that is emerging as the next-generation P2P Platform. The Framework presented here includes the basic functions of the SOA Framework. In addition to that, it is made easier to provide and tap into services compared with other frameworks while addressing security issues that may occur when using the service. One of its strong points is that each peer can not only be a service provider, a service client but also play a role as a service registry at the same time since P2P has been established in the form of a platform.

Wavelet-Based collaborative filtering for adapting changes in user behavior

Recommendation systems help users find the information, products and services they most want to find. Collaborative filtering is the method of making automatic predictions about the interest of a user by collecting interest information from many users, which has been very successful recommendation technique for recommendation systems in both research and practice. However, the traditional collaborative filtering is slow to detect the interest of a user changing with time as a case of user behavior and to adapt the changes, because the traditional collaborative filtering uses Pearsons correlation coefficient between users with the numerous values of property. In this paper, we apply the wavelet analysis to collaborative filtering in order to reveal the trends hidden in the interest of a user and propose the wavelet-based collaborative filtering for adapting changes in user behavior. The results of the performance evaluation show that the proposed wavelet-based collaborative filtering makes the improvement in the personalized recommendations.